PCB缺陷检测程序

QPSK modulation in matlab

Audio Feature Extraction routine. this routine extracts the attack time of an audio source, by extracting the timing (sample) of the highest peak. The archive inclues a routine to extract the fft and display it. Requires Voicebox for matlab.

GA-BP代码（遗传算法改进优化BP神经网络算法）(GA-BP code (genetic algorithm improved BP neural network algorithm))

阐述了负荷预测的应用研究,matlab实现了五类灰色关联度模型的计算,对于初学者具有参考意义。( It describes the application of load forecasting, matlab implements five gray correlation degree computing model, For beginners with a reference value.)

MAtlab - Matched Filter Test Generating Manchester Line Code for Random number (MAtlab- Matched Filter Test Generating Manchester Line Code for Random number )

在matlab环境中模拟单管燃油加热的管壁及油温的变化，分为在高温下考虑裂解和不考虑裂解俩种情况(In the matlab environment simulation single pipe fuel heating tube wall and the change of the oil temperature, divided into under high temperature to consider cracking and not consider cracking two kinds of cases)

采用matlab/simulink实现质子交换膜燃料电池的建模，可以使用作为分布式发电单元(matalb/simulink model the proton exchange membrane fuel cell.)

Matlab关于三相全控桥式整流电路的仿真模拟(Matlab simulation on fully controlled three-phase bridge rectifier circuit)

GPSC/A信号捕获算法，matlab程序，没有别的说明了。(GPS signal acquisition algorithm, matlab program)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)